Fault Detection for Rolling-Element Bearings Using Multivariate Statistical Process Control Methods

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

2 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Journal / PublicationIEEE Transactions on Instrumentation and Measurement
Online published22 Oct 2018
Publication statusOnline published - 22 Oct 2018


This paper proposes a new bearing fault detection framework that is based on multivariate statistical process control methods. In this framework, historical offline normal data are used to train the models and calculate the control limits of the monitored metrics. Then, bearings' new online data are the input to the trained models to obtain their monitoring metrics, which are compared with the control limits to determine the healthy status of bearings. Unlike most conventional methods, the proposed framework does not need any faulty data at the training stage. Therefore, the proposed framework is flexible and applicable to most practical cases in which few or even no faulty data are available at the training stage. Two bearings' life data sets are used to validate the proposed fault detection approach. Results show that the higher order cumulants analysis-based approach exhibits better performance in bearing fault detection when compared with principal component analysis-based and independent component analysis-based approach.

Research Area(s)

  • Data models, Fault detection, Feature extraction, higher order cumulants analysis (HCA), independent component analysis (ICA), Measurement, Monitoring, multivariate statistical process control (MSPC), Principal component analysis, principal component analysis (PCA), rolling-element bearing, vibration signal., Vibrations